Héctor-Gabriel Acosta-Mesa

Discovering interobserver variability in the cytodiagnosis of breast cancer using decision trees and Bayesian networks

We evaluate the performance of two decision tree procedures and four Bayesian network classifiers as potential decision support systems in the cytodiagnosis of breast cancer. In order to test their performance thoroughly, we use two real-world databases containing 692 cases and 322 cases collected by a single observer and 19 observers, respectively. The results show that, in general, there are considerable differences in all tests (accuracy, sensitivity, specificity, PV+, PV- and ROC) when a specific classifier uses the single-observer dataset compared to those when this same classifier uses the multiple-observer dataset. These results suggest that different observers see different things: a problem known as interobserver variability. We graphically unveil such a problem by presenting the structures of the decision trees and Bayesian networks resultant from running both databases.

Aceto-white temporal pattern classification using k-NN to identify precancerous cervical lesion in colposcopic images

After Pap smear test, colposcopy is the most used technique to diagnose cervical cancer due to its higher sensitivity and specificity. One of the most promising approaches to improve the colposcopic test is the use of the aceto-white temporal patterns intrinsic to the color changes in digital images. However, there is not a complete understanding of how to use them to segment colposcopic images. In this work, we used the classification algorithm k-NN over the entire length of the aceto-white temporal pattern to automatically discriminate between normal and abnormal cervical tissue, reaching a sensitivity of 71% and specificity of 59%.

Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions

In this work, we present a novel application of time series discretization using evolutionary programming for the classification of precancerous cervical lesions. The approach optimizes the number of intervals in which the length and amplitude of the time series should be compressed, preserving the important information for classification purposes. Using evolutionary programming, the search for a good discretization scheme is guided by a cost function which considers three criteria: the entropy regarding the classification, the complexity measured as the number of different strings needed to represent the complete data set, and the compression rate assessed as the length of the discrete representation. This discretization approach is evaluated using a time series data based on temporal patterns observed during a classical test used in cervical cancer detection; the classification accuracy reached by our method is compared with the well-known times series discretization algorithm SAX and the dimensionality reduction method PCA. Statistical analysis of the classification accuracy shows that the discrete representation is as efficient as the complete raw representation for the present application, reducing the dimensionality of the time series length by 97%. This representation is also very competitive in terms of classification accuracy when compared with similar approaches.

Improved multi-objective clustering with automatic determination of the number of clusters

The multi-objective clustering with automatic determination of the number of clusters (MOCK) approach is improved in this work by means of an empirical comparison of three multi-objective evolutionary algorithms added to MOCK instead of the original algorithm used in such approach. The results of two different experiments using seven real data sets from UCI repository are reported: (1) using two multi-objective optimization performance metrics (hypervolume and two-set coverage) and (2) using the F-measure and the silhouette coefficient to evaluate the clustering quality. The results are compared against the original version of MOCK and also against other algorithms representative of the state of the art. Such results indicate that the new versions are highly competitive and able to deal with different types of data sets.

A Windowing strategy for Distributed Data Mining optimized through GPUs

Abstract This paper introduces an optimized Windowing based strategy for inducing decision trees in Distributed Data Mining scenarios. Windowing consists in selecting a sample of the available training examples (the window) to induce a decision tree with an usual algorithm, e.g., J48; finding instances not covered by this tree (counter examples) in the remaining training examples, adding them to the window to induce a new tree; and repeating until a termination criterion is met. In this way, the number of training examples required to induce the tree is reduced considerably, while maintaining the expected accuracy levels; which is paid in terms of time performance. Our proposed enhancements solve this by searching for counter examples on GPUs and further reducing their number in the window. The resulting strategy is implemented in JaCa-DDM, our agents & artifacts tool for Distributed Data Mining, keeping the benefits of Windowing, while distributing the process and being faster than the traditional centralized approach, even performing similarly to Bagging and Random Forests in some cases. Experiments in data mining tasks are addressed, including a case study on pixel-based segmentation for the detection of precancerous cervical lesions on medical images.

An Image Registration Method for Colposcopic Images

A nonrigid body image registration method for spatiotemporal alignment of image sequences obtained from colposcopy examinations to detect precancerous lesions of the cervix is proposed in this paper. The approach is based on time series calculation for those pixels in the first image of the sequence and a division of such image into small windows. A search process is then carried out to find the window with the highest affinity in each image of the sequence and replace it with the window in the reference image. The affinity value is based on polynomial approximation of the time series computed and the search is bounded by a search radius which defines the neighborhood of each window. The proposed approach is tested in ten 310-frame real cases in two experiments: the first one to determine the best values for the window size and the search radius and the second one to compare the best obtained results with respect to four registration methods found in the specialized literature. The obtained results show a robust and competitive performance of the proposed approach with a significant lower time with respect to the compared methods.

Automatic classification of acetowhite temporal patterns to identify precursor lesions of cervical cancer

Cervical cancer has remained, until now, as a serious public health problem in developing countries. The most common method of screening is the Pap test or cytology. When abnormalities are reported in the result, the patient is referred to a dysplasia clinic for colposcopy. During this test, a solution of acetic acid is applied, which produces a color change in the tissue and is known as acetowhitening phenomenon. This reaction aims to obtaining a sample of tissue and its histological analysis let to establish a final diagnosis. During the colposcopy test, digital images can be acquired to analyze the behavior of the acetowhitening reaction from a temporal approach. In this way, we try to identify precursor lesions of cervical cancer through a process of automatic classification of acetowhite temporal patterns. In this paper, we present the performance analysis of three classification methods: kNN, Naive Bayes and C4.5. The results showed that there is similarity between some acetowhite temporal patterns of normal and abnormal tissues. Therefore we conclude that it is not sufficient to only consider the temporal dynamic of the acetowhitening reaction to establish a diagnosis by an automatic method. Information from cytologic, colposcopic and histopathologic disciplines should be integrated as well.

Semi-Automatic Analysis for Unidimensional Immunoblot Images to Discriminate Breast Cancer Cases Using Time Series Data Mining

Breast cancer (BC) is one of the leading causes of death in adult women worldwide and the best way to reduce mortality and improve prognosis is through early diagnosis. Thus, it is necessary to opt...

Biofunctionality of native and nano-structured blue corn starch in prediabetic Wistar rats

ABSTRACT The biofunctionality of native and nanostructured starch obtained from blue corn was evaluated on prediabetic Wistar rats. The surface of both types of starch was analyzed by atomic force microscopy (AFM). Total polyphenols content, antioxidant activity and digestibility were also evaluated. Prediabetes was induced by feeding a diet high in fat, carbohydrates and the administration of streptozotocin. Experimental design included a control group, prediabetic group and two prediabetic groups, one supplemented with native starch and the other with nanostructured starch. AFM analysis showed nano-cavities <5 nm in nanostructured starch. Nanostructured starch also had a higher content of total polyphenols, higher antioxidant activity and higher percentage of slow digestibility starch compared to native starch. Glucose, triglycerides 34 and insulin in the plasma increased significantly in the prediabetic group. Nanostructured starch administration decreased the levels of glucose and insulin in the plasma and therefore has potential as functional ingredient.

Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy

Efforts have been being made to improve the diagnostic performance of colposcopy, trying to help better diagnose cervical cancer, particularly in developing countries. However, improvements in a number of areas are still necessary, such as the time it takes to process the full digital image of the cervix, the performance of the computing systems used to identify different kinds of tissues, and biopsy sampling. In this paper, we explore three different, well-known automatic classification methods (k-Nearest Neighbors, Naïve Bayes, and C4.5), in addition to different data models that take full advantage of this information and improve the diagnostic performance of colposcopy based on acetowhite temporal patterns. Based on the ROC and PRC area scores, the k-Nearest Neighbors and discrete PLA representation performed better than other methods. The values of sensitivity, specificity, and accuracy reached using this method were 60% (95% CI 50–70), 79% (95% CI 71–86), and 70% (95% CI 60–80), respectively. The acetowhitening phenomenon is not exclusive to high-grade lesions, and we have found acetowhite temporal patterns of epithelial changes that are not precancerous lesions but that are similar to positive ones. These findings need to be considered when developing more robust computing systems in the future.